Overview

Brought to you by YData

Dataset statistics

Number of variables50
Number of observations98490
Missing cells183637
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.8 MiB
Average record size in memory392.0 B

Variable types

Text4
Categorical42
Numeric3
Boolean1

Alerts

Academic Pressure is highly overall correlated with Depression and 1 other fieldsHigh correlation
Age is highly overall correlated with Depression and 1 other fieldsHigh correlation
CGPA is highly overall correlated with Job Satisfaction and 18 other fieldsHigh correlation
Depression is highly overall correlated with Academic Pressure and 2 other fieldsHigh correlation
Job Satisfaction is highly overall correlated with CGPA and 2 other fieldsHigh correlation
Sleep Duration_1-3 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_1-6 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_3-6 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_4-6 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_45-48 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_49 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_6-8 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_8-9 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_9-11 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_9-5 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_9-6 hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_Indore is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_No is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_Pune is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_Sleep_Duration is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_Unhealthy is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Sleep Duration_Work_Study_Hours is highly overall correlated with CGPA and 1 other fieldsHigh correlation
Study Satisfaction is highly overall correlated with Job Satisfaction and 18 other fieldsHigh correlation
Work Pressure is highly overall correlated with CGPA and 2 other fieldsHigh correlation
Working Professional or Student is highly overall correlated with Academic Pressure and 4 other fieldsHigh correlation
Academic Pressure is highly imbalanced (61.9%) Imbalance
Dietary Habits is highly imbalanced (62.0%) Imbalance
Sleep Duration_1-2 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_1-3 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_1-6 hours is highly imbalanced (99.9%) Imbalance
Sleep Duration_10-11 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_2-3 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_3-4 hours is highly imbalanced (99.9%) Imbalance
Sleep Duration_3-6 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_4-5 hours is highly imbalanced (99.9%) Imbalance
Sleep Duration_4-6 hours is highly imbalanced (99.9%) Imbalance
Sleep Duration_40-45 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_45 is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_45-48 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_49 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_6-7 hours is highly imbalanced (99.9%) Imbalance
Sleep Duration_6-8 hours is highly imbalanced (99.9%) Imbalance
Sleep Duration_8 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_8-9 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_9-11 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_9-5 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_9-6 hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_Indore is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_Moderate is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_No is highly imbalanced (99.9%) Imbalance
Sleep Duration_Pune is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_Sleep_Duration is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_Unhealthy is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_Work_Study_Hours is highly imbalanced (> 99.9%) Imbalance
Sleep Duration_than 5 hours is highly imbalanced (> 99.9%) Imbalance
Profession has 25739 (26.1%) missing values Missing
CGPA has 78945 (80.2%) missing values Missing
Study Satisfaction has 78946 (80.2%) missing values Missing
Work/Study Hours has 8399 (8.5%) zeros Zeros

Reproduction

Analysis started2025-01-30 07:05:07.584736
Analysis finished2025-01-30 07:05:20.940449
Duration13.36 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Name
Text

Distinct363
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
2025-01-30T14:05:21.127969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.6427048
Min length2

Characters and Unicode

Total characters555750
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)0.1%

Sample

1st rowChirag
2nd rowArmaan
3rd rowAnirudh
4th rowVivan
5th rowRitik
ValueCountFrequency (%)
rohan 2227
 
2.3%
aarav 1643
 
1.7%
rupak 1467
 
1.5%
anvi 1440
 
1.5%
aaradhya 1427
 
1.4%
raghavendra 1300
 
1.3%
vani 1172
 
1.2%
ritvik 1139
 
1.2%
riya 1131
 
1.1%
shiv 1104
 
1.1%
Other values (353) 84441
85.7%
2025-01-30T14:05:21.445040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 113465
20.4%
i 57779
 
10.4%
h 51736
 
9.3%
n 37781
 
6.8%
r 33912
 
6.1%
s 24924
 
4.5%
A 23430
 
4.2%
v 22039
 
4.0%
R 16698
 
3.0%
y 15692
 
2.8%
Other values (39) 158294
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 555750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 113465
20.4%
i 57779
 
10.4%
h 51736
 
9.3%
n 37781
 
6.8%
r 33912
 
6.1%
s 24924
 
4.5%
A 23430
 
4.2%
v 22039
 
4.0%
R 16698
 
3.0%
y 15692
 
2.8%
Other values (39) 158294
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 555750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 113465
20.4%
i 57779
 
10.4%
h 51736
 
9.3%
n 37781
 
6.8%
r 33912
 
6.1%
s 24924
 
4.5%
A 23430
 
4.2%
v 22039
 
4.0%
R 16698
 
3.0%
y 15692
 
2.8%
Other values (39) 158294
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 555750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 113465
20.4%
i 57779
 
10.4%
h 51736
 
9.3%
n 37781
 
6.8%
r 33912
 
6.1%
s 24924
 
4.5%
A 23430
 
4.2%
v 22039
 
4.0%
R 16698
 
3.0%
y 15692
 
2.8%
Other values (39) 158294
28.5%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
Male
54289 
Female
44201 

Length

Max length6
Median length4
Mean length4.8975734
Min length4

Characters and Unicode

Total characters482362
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 54289
55.1%
Female 44201
44.9%

Length

2025-01-30T14:05:21.557065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:21.648090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 54289
55.1%
female 44201
44.9%

Most occurring characters

ValueCountFrequency (%)
e 142691
29.6%
a 98490
20.4%
l 98490
20.4%
M 54289
 
11.3%
F 44201
 
9.2%
m 44201
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 482362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 142691
29.6%
a 98490
20.4%
l 98490
20.4%
M 54289
 
11.3%
F 44201
 
9.2%
m 44201
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 482362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 142691
29.6%
a 98490
20.4%
l 98490
20.4%
M 54289
 
11.3%
F 44201
 
9.2%
m 44201
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 482362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 142691
29.6%
a 98490
20.4%
l 98490
20.4%
M 54289
 
11.3%
F 44201
 
9.2%
m 44201
 
9.2%

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.380475
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size769.6 KiB
2025-01-30T14:05:21.739110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median42
Q351
95-th percentile58
Maximum60
Range42
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.398445
Coefficient of variation (CV)0.3070406
Kurtosis-1.1495124
Mean40.380475
Median Absolute Deviation (MAD)10
Skewness-0.21841398
Sum3977073
Variance153.72145
MonotonicityNot monotonic
2025-01-30T14:05:21.848135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
56 3693
 
3.7%
49 3505
 
3.6%
38 3212
 
3.3%
53 3133
 
3.2%
57 3040
 
3.1%
47 2959
 
3.0%
46 2883
 
2.9%
54 2775
 
2.8%
18 2765
 
2.8%
51 2753
 
2.8%
Other values (33) 67772
68.8%
ValueCountFrequency (%)
18 2765
2.8%
19 1886
1.9%
20 2469
2.5%
21 1935
2.0%
22 1475
1.5%
23 2053
2.1%
24 2330
2.4%
25 2012
2.0%
26 1445
1.5%
27 1809
1.8%
ValueCountFrequency (%)
60 1787
1.8%
59 2626
2.7%
58 2071
2.1%
57 3040
3.1%
56 3693
3.7%
55 2008
2.0%
54 2775
2.8%
53 3133
3.2%
52 1800
1.8%
51 2753
2.8%

City
Text

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
2025-01-30T14:05:21.997168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.019403
Min length2

Characters and Unicode

Total characters691341
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)< 0.1%

Sample

1st rowLucknow
2nd rowMumbai
3rd rowKalyan
4th rowVaranasi
5th rowKalyan
ValueCountFrequency (%)
kalyan 4634
 
4.7%
patna 4099
 
4.2%
vasai-virar 4003
 
4.1%
ahmedabad 3966
 
4.0%
kolkata 3955
 
4.0%
meerut 3870
 
3.9%
visakhapatnam 3647
 
3.7%
ludhiana 3637
 
3.7%
rajkot 3621
 
3.7%
pune 3609
 
3.7%
Other values (69) 59453
60.4%
2025-01-30T14:05:22.256411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 153462
22.2%
r 52710
 
7.6%
n 47534
 
6.9%
i 40980
 
5.9%
d 34848
 
5.0%
e 33245
 
4.8%
u 29892
 
4.3%
h 26869
 
3.9%
o 22562
 
3.3%
t 22467
 
3.2%
Other values (39) 226772
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 691341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 153462
22.2%
r 52710
 
7.6%
n 47534
 
6.9%
i 40980
 
5.9%
d 34848
 
5.0%
e 33245
 
4.8%
u 29892
 
4.3%
h 26869
 
3.9%
o 22562
 
3.3%
t 22467
 
3.2%
Other values (39) 226772
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 691341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 153462
22.2%
r 52710
 
7.6%
n 47534
 
6.9%
i 40980
 
5.9%
d 34848
 
5.0%
e 33245
 
4.8%
u 29892
 
4.3%
h 26869
 
3.9%
o 22562
 
3.3%
t 22467
 
3.2%
Other values (39) 226772
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 691341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 153462
22.2%
r 52710
 
7.6%
n 47534
 
6.9%
i 40980
 
5.9%
d 34848
 
5.0%
e 33245
 
4.8%
u 29892
 
4.3%
h 26869
 
3.9%
o 22562
 
3.3%
t 22467
 
3.2%
Other values (39) 226772
32.8%

Working Professional or Student
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
1
78943 
0
19547 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters98490
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 78943
80.2%
0 19547
 
19.8%

Length

2025-01-30T14:05:22.352434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:22.425451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 78943
80.2%
0 19547
 
19.8%

Most occurring characters

ValueCountFrequency (%)
1 78943
80.2%
0 19547
 
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 78943
80.2%
0 19547
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 78943
80.2%
0 19547
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 78943
80.2%
0 19547
 
19.8%

Profession
Text

Missing 

Distinct60
Distinct (%)0.1%
Missing25739
Missing (%)26.1%
Memory size769.6 KiB
2025-01-30T14:05:22.553479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.691248
Min length2

Characters and Unicode

Total characters777799
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowMarketing Manager
2nd rowHR Manager
3rd rowPharmacist
4th rowData Scientist
5th rowChef
ValueCountFrequency (%)
teacher 17356
 
17.2%
consultant 6342
 
6.3%
writer 5529
 
5.5%
content 5529
 
5.5%
manager 5371
 
5.3%
analyst 4660
 
4.6%
architect 3052
 
3.0%
engineer 2920
 
2.9%
hr 2782
 
2.8%
pharmacist 2724
 
2.7%
Other values (59) 44476
44.1%
2025-01-30T14:05:22.816539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 101132
13.0%
a 72060
 
9.3%
r 70083
 
9.0%
t 69499
 
8.9%
n 62262
 
8.0%
c 43980
 
5.7%
i 41389
 
5.3%
h 31018
 
4.0%
s 30972
 
4.0%
27990
 
3.6%
Other values (39) 227414
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 777799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 101132
13.0%
a 72060
 
9.3%
r 70083
 
9.0%
t 69499
 
8.9%
n 62262
 
8.0%
c 43980
 
5.7%
i 41389
 
5.3%
h 31018
 
4.0%
s 30972
 
4.0%
27990
 
3.6%
Other values (39) 227414
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 777799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 101132
13.0%
a 72060
 
9.3%
r 70083
 
9.0%
t 69499
 
8.9%
n 62262
 
8.0%
c 43980
 
5.7%
i 41389
 
5.3%
h 31018
 
4.0%
s 30972
 
4.0%
27990
 
3.6%
Other values (39) 227414
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 777799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 101132
13.0%
a 72060
 
9.3%
r 70083
 
9.0%
t 69499
 
8.9%
n 62262
 
8.0%
c 43980
 
5.7%
i 41389
 
5.3%
h 31018
 
4.0%
s 30972
 
4.0%
27990
 
3.6%
Other values (39) 227414
29.2%

Academic Pressure
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
3.0
84194 
5.0
 
4384
4.0
 
3640
1.0
 
3344
2.0
 
2928

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 84194
85.5%
5.0 4384
 
4.5%
4.0 3640
 
3.7%
1.0 3344
 
3.4%
2.0 2928
 
3.0%

Length

2025-01-30T14:05:22.915560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:22.994577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 84194
85.5%
5.0 4384
 
4.5%
4.0 3640
 
3.7%
1.0 3344
 
3.4%
2.0 2928
 
3.0%

Most occurring characters

ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 84194
28.5%
5 4384
 
1.5%
4 3640
 
1.2%
1 3344
 
1.1%
2 2928
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 84194
28.5%
5 4384
 
1.5%
4 3640
 
1.2%
1 3344
 
1.1%
2 2928
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 84194
28.5%
5 4384
 
1.5%
4 3640
 
1.2%
1 3344
 
1.1%
2 2928
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 84194
28.5%
5 4384
 
1.5%
4 3640
 
1.2%
1 3344
 
1.1%
2 2928
 
1.0%

Work Pressure
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
3.0
34810 
2.0
17095 
4.0
15824 
5.0
15703 
1.0
15058 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 34810
35.3%
2.0 17095
17.4%
4.0 15824
16.1%
5.0 15703
15.9%
1.0 15058
15.3%

Length

2025-01-30T14:05:23.084598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:23.165616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 34810
35.3%
2.0 17095
17.4%
4.0 15824
16.1%
5.0 15703
15.9%
1.0 15058
15.3%

Most occurring characters

ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34810
 
11.8%
2 17095
 
5.8%
4 15824
 
5.4%
5 15703
 
5.3%
1 15058
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34810
 
11.8%
2 17095
 
5.8%
4 15824
 
5.4%
5 15703
 
5.3%
1 15058
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34810
 
11.8%
2 17095
 
5.8%
4 15824
 
5.4%
5 15703
 
5.3%
1 15058
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34810
 
11.8%
2 17095
 
5.8%
4 15824
 
5.4%
5 15703
 
5.3%
1 15058
 
5.1%

CGPA
Real number (ℝ)

High correlation  Missing 

Distinct326
Distinct (%)1.7%
Missing78945
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean7.6529559
Minimum5.03
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size769.6 KiB
2025-01-30T14:05:23.273024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.03
5-th percentile5.38
Q16.29
median7.77
Q38.92
95-th percentile9.89
Maximum10
Range4.97
Interquartile range (IQR)2.63

Descriptive statistics

Standard deviation1.4641066
Coefficient of variation (CV)0.19131256
Kurtosis-1.2275703
Mean7.6529559
Median Absolute Deviation (MAD)1.27
Skewness-0.068991887
Sum149577.02
Variance2.1436081
MonotonicityNot monotonic
2025-01-30T14:05:23.388050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.04 589
 
0.6%
9.96 305
 
0.3%
5.74 292
 
0.3%
7.25 253
 
0.3%
8.95 248
 
0.3%
9.21 240
 
0.2%
7.09 229
 
0.2%
7.88 227
 
0.2%
9.44 215
 
0.2%
7.77 194
 
0.2%
Other values (316) 16753
 
17.0%
(Missing) 78945
80.2%
ValueCountFrequency (%)
5.03 13
 
< 0.1%
5.06 9
 
< 0.1%
5.08 72
0.1%
5.09 12
 
< 0.1%
5.1 43
 
< 0.1%
5.11 81
0.1%
5.12 106
0.1%
5.14 14
 
< 0.1%
5.16 162
0.2%
5.17 8
 
< 0.1%
ValueCountFrequency (%)
10 37
 
< 0.1%
9.98 43
 
< 0.1%
9.97 91
 
0.1%
9.96 305
0.3%
9.95 90
 
0.1%
9.94 52
 
0.1%
9.93 188
0.2%
9.92 44
 
< 0.1%
9.91 85
 
0.1%
9.9 19
 
< 0.1%

Study Satisfaction
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing78946
Missing (%)80.2%
Memory size769.6 KiB
4.0
4491 
2.0
4101 
3.0
4062 
1.0
3780 
5.0
3110 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58632
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row5.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 4491
 
4.6%
2.0 4101
 
4.2%
3.0 4062
 
4.1%
1.0 3780
 
3.8%
5.0 3110
 
3.2%
(Missing) 78946
80.2%

Length

2025-01-30T14:05:23.488072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:23.577092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 4491
23.0%
2.0 4101
21.0%
3.0 4062
20.8%
1.0 3780
19.3%
5.0 3110
15.9%

Most occurring characters

ValueCountFrequency (%)
. 19544
33.3%
0 19544
33.3%
4 4491
 
7.7%
2 4101
 
7.0%
3 4062
 
6.9%
1 3780
 
6.4%
5 3110
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 19544
33.3%
0 19544
33.3%
4 4491
 
7.7%
2 4101
 
7.0%
3 4062
 
6.9%
1 3780
 
6.4%
5 3110
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 19544
33.3%
0 19544
33.3%
4 4491
 
7.7%
2 4101
 
7.0%
3 4062
 
6.9%
1 3780
 
6.4%
5 3110
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 19544
33.3%
0 19544
33.3%
4 4491
 
7.7%
2 4101
 
7.0%
3 4062
 
6.9%
1 3780
 
6.4%
5 3110
 
5.3%

Job Satisfaction
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
3.0
34827 
2.0
17304 
5.0
15932 
1.0
15743 
4.0
14684 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 34827
35.4%
2.0 17304
17.6%
5.0 15932
16.2%
1.0 15743
16.0%
4.0 14684
14.9%

Length

2025-01-30T14:05:23.678115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:23.762134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 34827
35.4%
2.0 17304
17.6%
5.0 15932
16.2%
1.0 15743
16.0%
4.0 14684
14.9%

Most occurring characters

ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34827
 
11.8%
2 17304
 
5.9%
5 15932
 
5.4%
1 15743
 
5.3%
4 14684
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34827
 
11.8%
2 17304
 
5.9%
5 15932
 
5.4%
1 15743
 
5.3%
4 14684
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34827
 
11.8%
2 17304
 
5.9%
5 15932
 
5.4%
1 15743
 
5.3%
4 14684
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 98490
33.3%
0 98490
33.3%
3 34827
 
11.8%
2 17304
 
5.9%
5 15932
 
5.4%
1 15743
 
5.3%
4 14684
 
5.0%

Dietary Habits
Categorical

Imbalance 

Distinct18
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size769.6 KiB
Moderate
34768 
Unhealthy
32437 
Healthy
31264 
Yes
 
2
More Healthy
 
2
Other values (13)
 
13

Length

Max length17
Median length12
Mean length8.0117174
Min length1

Characters and Unicode

Total characters789042
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowModerate
2nd rowHealthy
3rd rowUnhealthy
4th rowUnhealthy
5th rowHealthy

Common Values

ValueCountFrequency (%)
Moderate 34768
35.3%
Unhealthy 32437
32.9%
Healthy 31264
31.7%
Yes 2
 
< 0.1%
More Healthy 2
 
< 0.1%
Vegas 1
 
< 0.1%
1.0 1
 
< 0.1%
BSc 1
 
< 0.1%
No 1
 
< 0.1%
Male 1
 
< 0.1%
Other values (8) 8
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2025-01-30T14:05:23.876534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moderate 34768
35.3%
unhealthy 32437
32.9%
healthy 31269
31.7%
less 2
 
< 0.1%
no 2
 
< 0.1%
more 2
 
< 0.1%
yes 2
 
< 0.1%
vegas 1
 
< 0.1%
1.0 1
 
< 0.1%
bsc 1
 
< 0.1%
Other values (7) 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 133253
16.9%
a 98479
12.5%
t 98476
12.5%
h 96145
12.2%
l 63709
8.1%
y 63706
8.1%
r 34774
 
4.4%
o 34774
 
4.4%
M 34772
 
4.4%
d 34769
 
4.4%
Other values (21) 96185
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 789042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 133253
16.9%
a 98479
12.5%
t 98476
12.5%
h 96145
12.2%
l 63709
8.1%
y 63706
8.1%
r 34774
 
4.4%
o 34774
 
4.4%
M 34772
 
4.4%
d 34769
 
4.4%
Other values (21) 96185
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 789042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 133253
16.9%
a 98479
12.5%
t 98476
12.5%
h 96145
12.2%
l 63709
8.1%
y 63706
8.1%
r 34774
 
4.4%
o 34774
 
4.4%
M 34772
 
4.4%
d 34769
 
4.4%
Other values (21) 96185
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 789042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 133253
16.9%
a 98479
12.5%
t 98476
12.5%
h 96145
12.2%
l 63709
8.1%
y 63706
8.1%
r 34774
 
4.4%
o 34774
 
4.4%
M 34772
 
4.4%
d 34769
 
4.4%
Other values (21) 96185
12.2%

Degree
Text

Distinct97
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size769.6 KiB
2025-01-30T14:05:24.072579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length4.3890525
Min length1

Characters and Unicode

Total characters432269
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)0.1%

Sample

1st rowMA
2nd rowB.Com
3rd rowMCA
4th rowMBBS
5th rowBE
ValueCountFrequency (%)
class 10395
 
9.5%
12 10395
 
9.5%
b.ed 8176
 
7.5%
b.arch 6063
 
5.6%
b.com 5709
 
5.2%
b.pharm 4120
 
3.8%
bca 4012
 
3.7%
m.ed 3966
 
3.6%
mca 3638
 
3.3%
bba 3547
 
3.3%
Other values (90) 48875
44.9%
2025-01-30T14:05:24.382154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 59681
13.8%
M 42079
 
9.7%
. 39655
 
9.2%
C 25880
 
6.0%
A 24521
 
5.7%
h 21869
 
5.1%
s 20807
 
4.8%
c 19220
 
4.4%
a 17782
 
4.1%
E 16920
 
3.9%
Other values (44) 143855
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 59681
13.8%
M 42079
 
9.7%
. 39655
 
9.2%
C 25880
 
6.0%
A 24521
 
5.7%
h 21869
 
5.1%
s 20807
 
4.8%
c 19220
 
4.4%
a 17782
 
4.1%
E 16920
 
3.9%
Other values (44) 143855
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 59681
13.8%
M 42079
 
9.7%
. 39655
 
9.2%
C 25880
 
6.0%
A 24521
 
5.7%
h 21869
 
5.1%
s 20807
 
4.8%
c 19220
 
4.4%
a 17782
 
4.1%
E 16920
 
3.9%
Other values (44) 143855
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 59681
13.8%
M 42079
 
9.7%
. 39655
 
9.2%
C 25880
 
6.0%
A 24521
 
5.7%
h 21869
 
5.1%
s 20807
 
4.8%
c 19220
 
4.4%
a 17782
 
4.1%
E 16920
 
3.9%
Other values (44) 143855
33.3%

Have you ever had suicidal thoughts ?
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0
49853 
1
48637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters98490
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 49853
50.6%
1 48637
49.4%

Length

2025-01-30T14:05:24.480252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:24.553269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49853
50.6%
1 48637
49.4%

Most occurring characters

ValueCountFrequency (%)
0 49853
50.6%
1 48637
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49853
50.6%
1 48637
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49853
50.6%
1 48637
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49853
50.6%
1 48637
49.4%

Work/Study Hours
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2355264
Minimum0
Maximum12
Zeros8399
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size769.6 KiB
2025-01-30T14:05:24.630287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8490693
Coefficient of variation (CV)0.61728057
Kurtosis-1.2831916
Mean6.2355264
Median Absolute Deviation (MAD)4
Skewness-0.12045756
Sum614137
Variance14.815335
MonotonicityNot monotonic
2025-01-30T14:05:24.732311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 9855
10.0%
11 8895
9.0%
9 8845
9.0%
0 8399
8.5%
12 7933
8.1%
2 7494
 
7.6%
6 7345
 
7.5%
7 6958
 
7.1%
1 6914
 
7.0%
3 6680
 
6.8%
Other values (3) 19172
19.5%
ValueCountFrequency (%)
0 8399
8.5%
1 6914
7.0%
2 7494
7.6%
3 6680
6.8%
4 6408
6.5%
5 6507
6.6%
6 7345
7.5%
7 6958
7.1%
8 6257
6.4%
9 8845
9.0%
ValueCountFrequency (%)
12 7933
8.1%
11 8895
9.0%
10 9855
10.0%
9 8845
9.0%
8 6257
6.4%
7 6958
7.1%
6 7345
7.5%
5 6507
6.6%
4 6408
6.5%
3 6680
6.8%

Financial Stress
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size769.6 KiB
2.0
22036 
5.0
19749 
4.0
19425 
1.0
19120 
3.0
18159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295467
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row5.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 22036
22.4%
5.0 19749
20.1%
4.0 19425
19.7%
1.0 19120
19.4%
3.0 18159
18.4%
(Missing) 1
 
< 0.1%

Length

2025-01-30T14:05:24.839342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:24.922362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 22036
22.4%
5.0 19749
20.1%
4.0 19425
19.7%
1.0 19120
19.4%
3.0 18159
18.4%

Most occurring characters

ValueCountFrequency (%)
. 98489
33.3%
0 98489
33.3%
2 22036
 
7.5%
5 19749
 
6.7%
4 19425
 
6.6%
1 19120
 
6.5%
3 18159
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 98489
33.3%
0 98489
33.3%
2 22036
 
7.5%
5 19749
 
6.7%
4 19425
 
6.6%
1 19120
 
6.5%
3 18159
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 98489
33.3%
0 98489
33.3%
2 22036
 
7.5%
5 19749
 
6.7%
4 19425
 
6.6%
1 19120
 
6.5%
3 18159
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 98489
33.3%
0 98489
33.3%
2 22036
 
7.5%
5 19749
 
6.7%
4 19425
 
6.6%
1 19120
 
6.5%
3 18159
 
6.1%

Family History of Mental Illness
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.3 KiB
False
49438 
True
49052 
ValueCountFrequency (%)
False 49438
50.2%
True 49052
49.8%
2025-01-30T14:05:25.004382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Depression
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0
80593 
1
17897 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters98490
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 80593
81.8%
1 17897
 
18.2%

Length

2025-01-30T14:05:25.086402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:25.159195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 80593
81.8%
1 17897
 
18.2%

Most occurring characters

ValueCountFrequency (%)
0 80593
81.8%
1 17897
 
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80593
81.8%
1 17897
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80593
81.8%
1 17897
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80593
81.8%
1 17897
 
18.2%

Sleep Duration_1-2 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:25.240214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:25.312232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_1-3 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:25.392250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:25.465268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_1-6 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98487 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98487
> 99.9%
1.0 3
 
< 0.1%

Length

2025-01-30T14:05:25.545285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:25.618302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98487
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196977
66.7%
. 98490
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196977
66.7%
. 98490
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196977
66.7%
. 98490
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196977
66.7%
. 98490
33.3%
1 3
 
< 0.1%

Sleep Duration_10-11 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98488 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Length

2025-01-30T14:05:25.695322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:25.768340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Sleep Duration_2-3 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98488 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Length

2025-01-30T14:05:25.847357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:25.919376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Sleep Duration_3-4 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98482 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98482
> 99.9%
1.0 8
 
< 0.1%

Length

2025-01-30T14:05:26.104418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:26.176436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98482
> 99.9%
1.0 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Sleep Duration_3-6 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:26.254454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:26.328471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_4-5 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98485 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98485
> 99.9%
1.0 5
 
< 0.1%

Length

2025-01-30T14:05:26.405489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:26.478507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98485
> 99.9%
1.0 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Sleep Duration_4-6 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98485 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98485
> 99.9%
1.0 5
 
< 0.1%

Length

2025-01-30T14:05:26.556902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:26.629921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98485
> 99.9%
1.0 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196975
66.7%
. 98490
33.3%
1 5
 
< 0.1%

Sleep Duration_40-45 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:26.707940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:26.779583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_45
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98488 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Length

2025-01-30T14:05:26.858600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:26.930616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Sleep Duration_45-48 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:27.008634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.080650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_49 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:27.158668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.232684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_5-6 hours
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
75967 
1.0
22523 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 75967
77.1%
1.0 22523
 
22.9%

Length

2025-01-30T14:05:27.310702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.385718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 75967
77.1%
1.0 22523
 
22.9%

Most occurring characters

ValueCountFrequency (%)
0 174457
59.0%
. 98490
33.3%
1 22523
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174457
59.0%
. 98490
33.3%
1 22523
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174457
59.0%
. 98490
33.3%
1 22523
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174457
59.0%
. 98490
33.3%
1 22523
 
7.6%

Sleep Duration_6-7 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98482 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98482
> 99.9%
1.0 8
 
< 0.1%

Length

2025-01-30T14:05:27.465736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.537752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98482
> 99.9%
1.0 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196972
66.7%
. 98490
33.3%
1 8
 
< 0.1%

Sleep Duration_6-8 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98486 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98486
> 99.9%
1.0 4
 
< 0.1%

Length

2025-01-30T14:05:27.616830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.689847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98486
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Sleep Duration_7-8 hours
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
72591 
1.0
25899 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 72591
73.7%
1.0 25899
 
26.3%

Length

2025-01-30T14:05:27.767864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.841881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 72591
73.7%
1.0 25899
 
26.3%

Most occurring characters

ValueCountFrequency (%)
0 171081
57.9%
. 98490
33.3%
1 25899
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 171081
57.9%
. 98490
33.3%
1 25899
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 171081
57.9%
. 98490
33.3%
1 25899
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 171081
57.9%
. 98490
33.3%
1 25899
 
8.8%

Sleep Duration_8 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:27.920859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:27.994876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_8-9 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:28.071893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:28.145300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_9-11 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98488 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Length

2025-01-30T14:05:28.222788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:28.294811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Sleep Duration_9-5 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:28.375829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:28.448363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_9-6 hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:28.528381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:28.607398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_Indore
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:28.691417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:28.768435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_Less than 5 hours
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
71309 
1.0
27181 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 71309
72.4%
1.0 27181
 
27.6%

Length

2025-01-30T14:05:28.878459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:28.957476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 71309
72.4%
1.0 27181
 
27.6%

Most occurring characters

ValueCountFrequency (%)
0 169799
57.5%
. 98490
33.3%
1 27181
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 169799
57.5%
. 98490
33.3%
1 27181
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 169799
57.5%
. 98490
33.3%
1 27181
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 169799
57.5%
. 98490
33.3%
1 27181
 
9.2%

Sleep Duration_Moderate
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:29.039495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:29.112512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_More than 8 hours
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
75666 
1.0
22824 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 75666
76.8%
1.0 22824
 
23.2%

Length

2025-01-30T14:05:29.192530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:29.273552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 75666
76.8%
1.0 22824
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 174156
58.9%
. 98490
33.3%
1 22824
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174156
58.9%
. 98490
33.3%
1 22824
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174156
58.9%
. 98490
33.3%
1 22824
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174156
58.9%
. 98490
33.3%
1 22824
 
7.7%

Sleep Duration_No
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98486 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98486
> 99.9%
1.0 4
 
< 0.1%

Length

2025-01-30T14:05:29.356570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:29.428586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98486
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196976
66.7%
. 98490
33.3%
1 4
 
< 0.1%

Sleep Duration_Pune
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:29.507604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:29.582621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_Sleep_Duration
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:29.664082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:29.740099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_Unhealthy
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98488 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Length

2025-01-30T14:05:29.827118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:29.901135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98488
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196978
66.7%
. 98490
33.3%
1 2
 
< 0.1%

Sleep Duration_Work_Study_Hours
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:29.985154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:30.059170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Sleep Duration_than 5 hours
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
0.0
98489 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Length

2025-01-30T14:05:30.139188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T14:05:30.219206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98489
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 196979
66.7%
. 98490
33.3%
1 1
 
< 0.1%

Interactions

2025-01-30T14:05:18.985821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:18.493712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:18.752769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:19.062839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:18.587732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:18.825786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:19.145858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:18.675752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T14:05:18.898801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-30T14:05:30.314228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Academic PressureAgeCGPADepressionDietary HabitsFamily History of Mental IllnessFinancial StressGenderHave you ever had suicidal thoughts ?Job SatisfactionSleep Duration_1-2 hoursSleep Duration_1-3 hoursSleep Duration_1-6 hoursSleep Duration_10-11 hoursSleep Duration_2-3 hoursSleep Duration_3-4 hoursSleep Duration_3-6 hoursSleep Duration_4-5 hoursSleep Duration_4-6 hoursSleep Duration_40-45 hoursSleep Duration_45Sleep Duration_45-48 hoursSleep Duration_49 hoursSleep Duration_5-6 hoursSleep Duration_6-7 hoursSleep Duration_6-8 hoursSleep Duration_7-8 hoursSleep Duration_8 hoursSleep Duration_8-9 hoursSleep Duration_9-11 hoursSleep Duration_9-5 hoursSleep Duration_9-6 hoursSleep Duration_IndoreSleep Duration_Less than 5 hoursSleep Duration_ModerateSleep Duration_More than 8 hoursSleep Duration_NoSleep Duration_PuneSleep Duration_Sleep_DurationSleep Duration_UnhealthySleep Duration_Work_Study_HoursSleep Duration_than 5 hoursStudy SatisfactionWork PressureWork/Study HoursWorking Professional or Student
Academic Pressure1.0000.2910.0430.5040.0360.0160.0420.0090.1570.2780.0000.0000.0000.0090.0090.0000.0000.0010.0000.0150.0090.0000.0000.0090.0030.0000.0050.0170.0000.0000.0000.0000.0000.0290.0170.0190.0000.0000.0000.0000.0000.0170.0780.2790.0620.828
Age0.2911.0000.0100.6230.0300.0140.0570.0550.1820.2260.0050.0010.0000.0030.0000.0030.0000.0020.0000.0050.0040.0000.0000.0240.0000.0040.0240.0050.0060.0020.0000.0000.0000.0660.0060.0360.0030.0000.0060.0000.0000.0050.0330.234-0.1130.699
CGPA0.0430.0101.0000.0590.0100.0000.0150.0700.0321.0000.0151.0001.0000.0040.0170.0051.0000.0111.0000.0150.0081.0001.0000.0270.0181.0000.0370.0081.0001.0001.0001.0001.0000.0370.0000.0241.0001.0001.0001.0001.0000.0000.0331.0000.0050.025
Depression0.5040.6230.0591.0000.1510.0150.2330.0070.3500.3460.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0040.0000.0000.0000.0000.0000.0000.0840.0000.0610.0000.0000.0000.0000.0000.0000.1640.3570.2060.521
Dietary Habits0.0360.0300.0100.1511.0000.0000.0250.0390.0680.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0140.0000.0000.0000.0000.0000.0000.0140.0000.0160.0000.0000.0000.0000.0000.0000.0180.0180.0110.053
Family History of Mental Illness0.0160.0140.0000.0150.0001.0000.0130.0140.0100.0150.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0120.014
Financial Stress0.0420.0570.0150.2330.0250.0131.0000.0100.0940.0320.0000.0000.0040.0000.0000.0000.0010.0000.0000.0000.0000.0010.0010.0220.0000.0000.0190.0010.0000.0050.0020.0010.0010.0360.0010.0080.0000.0000.0020.0000.0020.0000.0360.0260.0230.064
Gender0.0090.0550.0700.0070.0390.0140.0101.0000.0100.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0180.007
Have you ever had suicidal thoughts ?0.1570.1820.0320.3500.0680.0100.0940.0101.0000.1010.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0230.0000.0000.0000.0000.0000.0000.0900.0980.0670.137
Job Satisfaction0.2780.2261.0000.3460.0170.0150.0320.0120.1011.0000.0000.0030.0060.0000.0000.0000.0030.0000.0000.0000.0000.0040.0030.0120.0000.0000.0050.0000.0030.0020.0030.0040.0030.0270.0000.0110.0030.0000.0000.0000.0000.0001.0000.2360.0480.673
Sleep Duration_1-2 hours0.0000.0050.0150.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sleep Duration_1-3 hours0.0000.0011.0000.0000.0000.0000.0000.0000.0000.0030.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0040.0070.000
Sleep Duration_1-6 hours0.0000.0001.0000.0000.0000.0020.0040.0000.0000.0060.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0110.000
Sleep Duration_10-11 hours0.0090.0030.0040.0060.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.005
Sleep Duration_2-3 hours0.0090.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sleep Duration_3-4 hours0.0000.0030.0050.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0030.0000.0000.0000.0000.0000.0000.0030.0000.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.000
Sleep Duration_3-6 hours0.0000.0001.0000.0000.0000.0000.0010.0000.0000.0030.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0030.0000.000
Sleep Duration_4-5 hours0.0010.0020.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0060.000
Sleep Duration_4-6 hours0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Sleep Duration_40-45 hours0.0150.0050.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0080.000
Sleep Duration_450.0090.0040.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.000
Sleep Duration_45-48 hours0.0000.0001.0000.0000.0000.0000.0010.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0040.0000.000
Sleep Duration_49 hours0.0000.0001.0000.0000.0000.0000.0010.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0040.0030.000
Sleep Duration_5-6 hours0.0090.0240.0270.0230.0100.0000.0220.0060.0090.0120.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0001.0000.0020.0000.3250.0000.0000.0000.0000.0000.0000.3360.0000.2990.0000.0000.0000.0000.0000.0000.0180.0110.0170.009
Sleep Duration_6-7 hours0.0030.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0021.0000.0000.0030.0000.0000.0000.0000.0000.0000.0030.0000.0020.0000.0000.0000.0000.0000.0000.0160.0030.0000.004
Sleep Duration_6-8 hours0.0000.0041.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Sleep Duration_7-8 hours0.0050.0240.0370.0040.0140.0000.0190.0050.0000.0050.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.3250.0030.0001.0000.0000.0000.0000.0000.0000.0000.3690.0000.3280.0000.0000.0000.0000.0000.0000.0090.0000.0300.000
Sleep Duration_8 hours0.0170.0050.0080.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sleep Duration_8-9 hours0.0000.0061.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0040.0030.000
Sleep Duration_9-11 hours0.0000.0021.0000.0000.0000.0000.0050.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0020.0050.000
Sleep Duration_9-5 hours0.0000.0001.0000.0000.0000.0000.0020.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0030.0070.000
Sleep Duration_9-6 hours0.0000.0001.0000.0000.0000.0000.0010.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Sleep Duration_Indore0.0000.0001.0000.0000.0000.0000.0010.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0030.0000.000
Sleep Duration_Less than 5 hours0.0290.0660.0370.0840.0140.0000.0360.0000.0320.0270.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.3360.0030.0000.3690.0000.0000.0000.0000.0000.0001.0000.0000.3390.0000.0000.0000.0000.0000.0000.0220.0300.0090.026
Sleep Duration_Moderate0.0170.0060.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0070.000
Sleep Duration_More than 8 hours0.0190.0360.0240.0610.0160.0000.0080.0000.0230.0110.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.2990.0020.0000.3280.0000.0000.0000.0000.0000.0000.3390.0001.0000.0000.0000.0000.0000.0000.0000.0180.0210.0290.016
Sleep Duration_No0.0000.0031.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0030.000
Sleep Duration_Pune0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.0040.0000.000
Sleep Duration_Sleep_Duration0.0000.0061.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0030.000
Sleep Duration_Unhealthy0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0000.0000.000
Sleep Duration_Work_Study_Hours0.0000.0001.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0040.0060.000
Sleep Duration_than 5 hours0.0170.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0030.000
Study Satisfaction0.0780.0330.0330.1640.0180.0000.0360.0260.0901.0000.0001.0001.0000.0000.0000.0031.0000.0081.0000.0080.0001.0001.0000.0180.0161.0000.0090.0001.0001.0001.0001.0001.0000.0220.0080.0181.0001.0001.0001.0001.0000.0001.0001.0000.0190.000
Work Pressure0.2790.2341.0000.3570.0180.0150.0260.0000.0980.2360.0000.0040.0000.0000.0000.0000.0030.0000.0000.0000.0000.0040.0040.0110.0030.0000.0000.0000.0040.0020.0030.0000.0030.0300.0000.0210.0000.0040.0000.0000.0040.0001.0001.0000.0490.673
Work/Study Hours0.062-0.1130.0050.2060.0110.0120.0230.0180.0670.0480.0000.0070.0110.0000.0000.0000.0000.0060.0000.0080.0040.0000.0030.0170.0000.0000.0300.0000.0030.0050.0070.0000.0000.0090.0070.0290.0030.0000.0030.0000.0060.0030.0190.0491.0000.141
Working Professional or Student0.8280.6990.0250.5210.0530.0140.0640.0070.1370.6730.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0040.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0160.0000.0000.0000.0000.0000.0000.0000.6730.1411.000

Missing values

2025-01-30T14:05:19.335900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-30T14:05:20.043245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-30T14:05:20.673390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NameGenderAgeCityWorking Professional or StudentProfessionAcademic PressureWork PressureCGPAStudy SatisfactionJob SatisfactionDietary HabitsDegreeHave you ever had suicidal thoughts ?Work/Study HoursFinancial StressFamily History of Mental IllnessDepressionSleep Duration_1-2 hoursSleep Duration_1-3 hoursSleep Duration_1-6 hoursSleep Duration_10-11 hoursSleep Duration_2-3 hoursSleep Duration_3-4 hoursSleep Duration_3-6 hoursSleep Duration_4-5 hoursSleep Duration_4-6 hoursSleep Duration_40-45 hoursSleep Duration_45Sleep Duration_45-48 hoursSleep Duration_49 hoursSleep Duration_5-6 hoursSleep Duration_6-7 hoursSleep Duration_6-8 hoursSleep Duration_7-8 hoursSleep Duration_8 hoursSleep Duration_8-9 hoursSleep Duration_9-11 hoursSleep Duration_9-5 hoursSleep Duration_9-6 hoursSleep Duration_IndoreSleep Duration_Less than 5 hoursSleep Duration_ModerateSleep Duration_More than 8 hoursSleep Duration_NoSleep Duration_PuneSleep Duration_Sleep_DurationSleep Duration_UnhealthySleep Duration_Work_Study_HoursSleep Duration_than 5 hours
0ChiragMale29.0Lucknow0NaN3.03.08.592.03.0ModerateMA11.03.0No10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1ArmaanMale57.0Mumbai1Marketing Manager3.05.0NaNNaN3.0HealthyB.Com04.02.0No00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
2AnirudhMale56.0Kalyan1HR Manager3.04.0NaNNaN2.0UnhealthyMCA01.01.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3VivanMale56.0Varanasi1Pharmacist3.03.0NaNNaN3.0UnhealthyMBBS19.05.0No00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
4RitikMale38.0Kalyan1Data Scientist3.04.0NaNNaN1.0HealthyBE12.02.0Yes10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
5AaradhyaFemale32.0Ahmedabad1Chef3.04.0NaNNaN2.0ModerateBHM10.05.0Yes10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
6NainaFemale25.0Kalyan0NaN2.03.08.501.03.0ModerateB.Arch112.05.0No10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.0
7ZoyaFemale49.0Delhi1Civil Engineer3.02.0NaNNaN4.0ModerateBSc03.04.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
8NishantMale28.0Rajkot0NaN5.03.08.585.03.0HealthyB.Arch12.05.0Yes10.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
9AbhinavMale40.0Kolkata1Architect3.03.0NaNNaN5.0UnhealthyB.Arch13.01.0No00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
NameGenderAgeCityWorking Professional or StudentProfessionAcademic PressureWork PressureCGPAStudy SatisfactionJob SatisfactionDietary HabitsDegreeHave you ever had suicidal thoughts ?Work/Study HoursFinancial StressFamily History of Mental IllnessDepressionSleep Duration_1-2 hoursSleep Duration_1-3 hoursSleep Duration_1-6 hoursSleep Duration_10-11 hoursSleep Duration_2-3 hoursSleep Duration_3-4 hoursSleep Duration_3-6 hoursSleep Duration_4-5 hoursSleep Duration_4-6 hoursSleep Duration_40-45 hoursSleep Duration_45Sleep Duration_45-48 hoursSleep Duration_49 hoursSleep Duration_5-6 hoursSleep Duration_6-7 hoursSleep Duration_6-8 hoursSleep Duration_7-8 hoursSleep Duration_8 hoursSleep Duration_8-9 hoursSleep Duration_9-11 hoursSleep Duration_9-5 hoursSleep Duration_9-6 hoursSleep Duration_IndoreSleep Duration_Less than 5 hoursSleep Duration_ModerateSleep Duration_More than 8 hoursSleep Duration_NoSleep Duration_PuneSleep Duration_Sleep_DurationSleep Duration_UnhealthySleep Duration_Work_Study_HoursSleep Duration_than 5 hours
98480AaravMale48.0Bhopal1UX/UI Designer3.04.0NaNNaN2.0ModerateBE12.05.0No00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
98481IshaaniFemale37.0Surat1Teacher3.02.0NaNNaN2.0HealthyB.Ed111.02.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
98482RaghavendraMale47.0Faridabad1Pilot3.04.0NaNNaN5.0HealthyBCA08.04.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
98483ShivMale45.0Nagpur1Consultant3.03.0NaNNaN3.0UnhealthyMSc110.05.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
98484AadhyaFemale31.0Faridabad0NaN1.03.08.955.03.0ModerateMD010.01.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
98485ShivMale60.0Mumbai1Teacher3.03.0NaNNaN2.0UnhealthyB.Arch00.02.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
98486AaradhyaFemale56.0Vasai-Virar1NaN3.02.0NaNNaN3.0ModerateClass 12010.03.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.0
98487DiyaFemale60.0Meerut1Teacher3.03.0NaNNaN3.0UnhealthyMD19.01.0Yes00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.0
98488RupakMale21.0Kalyan0NaN5.03.08.621.03.0UnhealthyB.Arch18.02.0No10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
98489DiyaFemale43.0Meerut1Architect3.01.0NaNNaN4.0HealthyB.Arch10.05.0No00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0